Toqua

Better models to accelerate the decarbonisation

Belgium

Problem we are solving
In the coming years, shipping faces the challenge of making its fleet more carbon-efficient every year to comply with emission reduction regulations. The easiest option to reach these short-term decarbonization targets are operational optimizations (weather routing, maintenance optimization, speed optimization, etc.) and retrofits (air lubrication, flettner rotor, propeller attachments, premium paints, etc.).

The problem is that you can’t manage what you can’t measure.
Today's traditional ship performance modeling techniques (sea trial curves, ISO19030, Noon Report averages, ISO15016, ...) have average errors of around 10%-20%, making it impossible to measure and validate these single digit percentage savings. Factors like waves, wind, currents, loading conditions, fouling, etc. add noise to the modeling approach and make it impossible to accurately model a ship’s performance and validate fuel savings (as shown in the 2 figures below).

Finally, due to rising fuel prices, it’s not only compliance with emission regulations that’s a motivating factor, but also cost savings that form a main driver for better technology in this area.

Sustainable Impact on Blue Ecconomy
- API first - we spend 100% of our resources to make the models better
- Focus on sensor data - we unlock the value of this data by providing high accuracy score for the models
- Physics informed ML - hybrid approach as it is data driven but still takes into account the physics

Financial Needs~€2MIL